library(magrittr)
library(tidyverse)
library(Seurat)
library(readxl)
library(cowplot)
library(colorblindr)
library(viridis)
library(progeny)
library(destiny)
coi <- params$cell_type_super
cell_sort <- params$cell_sort
cell_type_major <- params$cell_type_major
louvain_resolution <- params$louvain_resolution
louvain_cluster <- params$louvain_cluster
### load all data ---------------------------------
source("_src/global_vars.R")
# seu_obj <- read_rds(paste0("/work/shah/isabl_data_lake/analyses/16/52/1652/celltypes/", coi, "_processed.rds"))
seu_obj <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/outs_pre/", coi, "_seurat_", louvain_resolution, ".rds"))
# seu_obj <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_highqc.rds"))
myfeatures <- c("umapharmony_1", "umapharmony_2", "sample", louvain_cluster, "doublet", "nCount_RNA", "nFeature_RNA", "percent.mt", "doublet_score")
plot_data_wrapper <- function(cluster_res) {
cluster_res <- enquo(cluster_res)
as_tibble(FetchData(seu_obj, myfeatures)) %>%
left_join(meta_tbl, by = "sample") %>%
rename(cluster = !!cluster_res) %>%
mutate(cluster = as.character(cluster),
tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite)))
}
plot_data <- plot_data_wrapper(louvain_cluster)
helper_f <- function(x) ifelse(is.na(x), "", x)
markers_v7_super[[coi]] %>%
group_by(subtype) %>%
mutate(rank = row_number(gene)) %>%
spread(subtype, gene) %>%
mutate_all(.funs = helper_f) %>%
formattable::formattable()
| rank | Activated.CAF.IGF1 | Activated.CAF.ISG | Activated.CAF.TGFb | Angiogenic.CAF | Cycling.CAF | dissociated | Early.CAF.1 | Early.CAF.2 | Mesothelial.CAF.IL1 | Pericyte |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | APOE | CXCL10 | ACTA2 | ANGPTL4 | CDC20 | IGKC | ACKR4 | C7 | AQP1 | A2M |
| 2 | ATF3 | IFI44L | COL11A1 | BNIP3 | CDK1 | MALAT1 | APOD | CADPS | C3 | ACTA2 |
| 3 | CYR61 | IFIT1 | COL12A1 | CA12 | MKI67 | MTRNR2L12 | C7 | IGFBP5 | CALB2 | ADIRF |
| 4 | FBLN1 | IFIT2 | COL5A1 | CA7 | PTTG1 | MTRNR2L8 | CFD | LAMP5 | CCDC80 | CAV1 |
| 5 | IGF1 | IFIT3 | COL5A2 | EGLN3 | TOP2A | SERPINF1 | GSN | PEG3 | CLDN1 | CCDC102B |
| 6 | ISG15 | COL6A1 | ENO1 | MGP | RBP1 | EZR | COL4A1 | |||
| 7 | MX1 | COL6A3 | ENO2 | PEG3 | RNASE1 | HP | COL4A2 | |||
| 8 | RSAD2 | FAP | HILPDA | KRT18 | CRIP1 | |||||
| 9 | FN1 | LDHA | KRT19 | HIGD1B | ||||||
| 10 | MMP11 | VEGFA | KRT8 | IGFBP7 | ||||||
| 11 | MMP13 | PRG4 | MCAM | |||||||
| 12 | UPK3B | MEF2C | ||||||||
| 13 | MHY11 | |||||||||
| 14 | NDUFA4L2 | |||||||||
| 15 | PP1R14A | |||||||||
| 16 | RGS5 |
## define patient specific clusters
patient_specific_clusters <- plot_data %>%
group_by(patient_id_short, cluster) %>%
tally %>%
group_by(cluster) %>%
mutate(nrel = n / sum(n),
ntotal = sum(n)) %>%
arrange(desc(nrel)) %>%
distinct(cluster, .keep_all = T) %>%
filter(nrel > 0.5) %>%
ungroup %>%
mutate(cluster_label_ps = make.names(paste0("OV.", patient_id_short, ".specific"), unique = T),
cluster = as.numeric(cluster)) %>%
distinct(cluster, cluster_label_ps)
# marker_tbl <- read_tsv(paste0("/work/shah/isabl_data_lake/analyses/16/52/1652/celltypes/", coi, "_markers.tsv")) %>%
# filter(resolution == louvain_resolution)
marker_tbl <- read_tsv(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/outs_pre/", coi, "_markers_", louvain_resolution, ".tsv"))
# marker_tbl <- read_tsv(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_highqc_markers_02.tsv"))
## Hypergeometric test --------------------------------------
test_set <- marker_tbl %>%
group_by(cluster) %>%
filter(!str_detect(gene, "^RPS|^RPL")) %>%
slice(1:50) %>%
mutate(k = length(cluster)) %>%
ungroup %>%
select(cluster, gene, k) %>%
mutate(join_helper = 1) %>%
group_by(cluster, join_helper, k) %>%
nest(test_set = gene)
markers_doub_tbl <- markers_v7 %>%
enframe("subtype", "gene") %>%
filter(!(subtype %in% unique(c(coi, cell_type_major)))) %>%
unnest(gene) %>%
group_by(gene) %>%
filter(length(gene) == 1) %>%
mutate(subtype = paste0("doublet.", subtype)) %>%
bind_rows(tibble(subtype = "Mito.high", gene = grep("^MT-", rownames(seu_obj), value = T)))
ref_set <- markers_v7_super[[coi]] %>%
bind_rows(markers_doub_tbl) %>%
group_by(subtype) %>%
mutate(m = length(gene),
n = length(rownames(seu_obj))-m,
join_helper = 1) %>%
group_by(subtype, m, n, join_helper) %>%
nest(ref_set = gene)
hyper_tbl <- test_set %>%
left_join(ref_set, by = "join_helper") %>%
group_by(cluster, subtype, m, n, k) %>%
do(q = length(intersect(unlist(.$ref_set), unlist(.$test_set)))) %>%
mutate(pval = 1-phyper(q = q, m = m, n = n, k = k)) %>%
ungroup %>%
mutate(qval = p.adjust(pval, "BH"),
sig = qval < 0.01)
# hyper_tbl %>%
# group_by(subtype) %>%
# filter(any(qval < 0.01)) %>%
# ggplot(aes(subtype, -log10(qval), fill = sig)) +
# geom_bar(stat = "identity") +
# facet_wrap(~cluster) +
# coord_flip()
low_rank <- str_detect(unique(hyper_tbl$subtype), "doublet|dissociated|Mito.high|OV\\.[0-9]")
subtype_lvl <- c(sort(unique(hyper_tbl$subtype)[!low_rank]), sort(unique(hyper_tbl$subtype)[low_rank]))
cluster_label_tbl <- hyper_tbl %>%
mutate(subtype = ordered(subtype, levels = subtype_lvl)) %>%
arrange(qval, subtype) %>%
group_by(cluster) %>%
slice(1) %>%
mutate(subtype = ifelse(sig, as.character(subtype), paste0("unknown_", cluster))) %>%
select(cluster, cluster_label = subtype) %>%
ungroup %>%
mutate(cluster_label = make.unique(cluster_label, sep = "_")) %>%
left_join(patient_specific_clusters, by = "cluster") %>%
mutate(cluster_label = ifelse(is.na(cluster_label_ps), cluster_label, cluster_label_ps)) %>%
select(-cluster_label_ps)
seu_obj$cluster_label <- unname(deframe(cluster_label_tbl)[as.character(unlist(seu_obj[[paste0("RNA_snn_res.", louvain_resolution)]]))])
plot_data$cluster_label <- seu_obj$cluster_label
cluster_n_tbl <- seu_obj$cluster_label %>%
table() %>%
enframe("cluster_label", "cluster_n") %>%
mutate(cluster_nrel = cluster_n/sum(cluster_n))
marker_sheet <- marker_tbl %>%
left_join(cluster_label_tbl, by = "cluster") %>%
group_by(cluster_label) %>%
filter(!str_detect(gene, "^RPS|^RPL")) %>%
slice(1:50) %>%
mutate(rank = row_number(-avg_logFC)) %>%
select(cluster_label, gene, rank) %>%
ungroup %>%
mutate(cluster_label = ordered(cluster_label, levels = unique(c(names(clrs$cluster_label[[coi]]), sort(cluster_label))))) %>%
spread(cluster_label, gene) %>%
mutate_all(.funs = helper_f)
marker_tbl_annotated <- marker_tbl %>%
left_join(cluster_label_tbl, by = "cluster") %>%
left_join(cluster_n_tbl, by = "cluster_label") %>%
select(-cluster, -resolution) %>%
mutate(cluster_label = ordered(cluster_label, levels = unique(c(names(clrs$cluster_label[[coi]]), sort(cluster_label))))) %>%
arrange(cluster_label, -avg_logFC, p_val_adj)
write_tsv(marker_sheet, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_marker_sheet.tsv"))
write_tsv(marker_sheet, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/supplementary_tables/", coi, "_marker_sheet.tsv"))
write_tsv(marker_tbl_annotated, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_marker_table_annotated.tsv"))
write_tsv(marker_tbl_annotated, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/supplementary_tables/", coi, "_marker_table_annotated.tsv"))
formattable::formattable(marker_sheet)
| rank | Early.CAF.1 | Early.CAF.2 | Activated.CAF.ISG | Activated.CAF.TGFb | Activated.CAF.IGF1 | Angiogenic.CAF | Mesothelial.CAF.IL1 | Cycling.CAF | Pericyte | doublet.Endothelial.cell | doublet.Monocyte | doublet.Ovarian.cancer.cell | OV.068.specific | OV.075.specific | OV.105.specific | OV.105.specific.1 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | CFD | C7 | CXCL10 | MMP11 | APOE | MT3 | HP | CENPF | COL4A1 | VWF | SPP1 | MMP7 | IGKC | MMP1 | TIMP1 | SLPI |
| 2 | APOD | RBP1 | ISG15 | CTHRC1 | APOC1 | SERPINE1 | SLPI | H2AFZ | RGS5 | FABP4 | SRGN | TFPI2 | FABP4 | RARRES2 | MMP9 | WFDC2 |
| 3 | C7 | IGFBP5 | IFIT1 | COL11A1 | IGF1 | ANGPTL4 | PRG4 | TOP2A | COL4A2 | A2M | CCL4 | KRT7 | COMP | DIO2 | MT2A | CXCL1 |
| 4 | MGP | RNASE1 | IFIT3 | POSTN | C3 | PLIN2 | KRT19 | MKI67 | PPP1R14A | PECAM1 | C1QB | SPINT2 | SFRP2 | MMP11 | APOC1 | FDCSP |
| 5 | GSN | PEG3 | RSAD2 | CTSK | CCDC80 | HILPDA | PLA2G2A | TUBA1B | IGFBP7 | PLVAP | C1QA | LCN2 | FGF7 | TDO2 | MIF | CXCL2 |
| 6 | GPX3 | CADPS | MX1 | COL1A2 | CXCL2 | NDRG1 | KRT18 | STMN1 | NDUFA4L2 | CLDN5 | TYROBP | CLDN3 | RBP7 | ISG15 | ANGPTL4 | PLAT |
| 7 | ADH1B | LAMP5 | IFIT2 | COL12A1 | CXCL12 | VEGFA | EZR | NUSAP1 | MCAM | EGFL7 | RGS1 | EPCAM | CILP | TNFSF10 | SEC61G | MT2A |
| 8 | TNXB | COLEC11 | IFI44L | VCAN | FBLN1 | IGFBP3 | UPK3B | PTTG1 | ADIRF | CD93 | CCL5 | CD24 | MGP | TMEM158 | SERPINB4 | TIMP1 |
| 9 | PLA2G2A | HOPX | CXCL11 | COL1A1 | PTGDS | LOX | KRT8 | HMGN2 | CCDC102B | ADGRL4 | CXCR4 | IGKC | GREM1 | RSAD2 | MT1X | SERPINB2 |
| 10 | PI16 | TSPAN8 | TNFSF10 | COL6A3 | CLU | MT2A | TM4SF1 | HIST1H4C | A2M | RBP7 | CCL3 | WFDC2 | APOE | IGFBP2 | G0S2 | CXCL6 |
| 11 | DPT | FHL2 | STAT1 | RGCC | CADM3 | ERO1A | C3 | PCLAF | ACTA2 | CLEC14A | C1QC | GPRC5A | CD36 | MMP13 | MT1E | NAMPT |
| 12 | SFRP1 | TCEAL4 | MX2 | ISLR | SELENOP | HSPA5 | CALB2 | BIRC5 | COL18A1 | CD36 | FCER1G | C19orf33 | IGF1 | WNT5A | PI15 | SERPINB4 |
| 13 | WISP2 | SPARCL1 | PARP14 | AEBP1 | RARRES1 | NAMPT | ITLN1 | CCNB1 | MEF2C | CD34 | PTPRC | CD9 | CYP1B1 | SULF1 | FTH1 | IL6 |
| 14 | SRPX | NR2F2 | OAS1 | COL6A1 | CLDN1 | BNIP3 | PROCR | ASPM | HIGD1B | PCDH17 | LYZ | KRT19 | TSHZ2 | BST2 | SH3BGRL3 | MT1G |
| 15 | SFRP2 | ABLIM1 | IFI6 | SFRP4 | IER2 | GAPDH | CLDN1 | TYMS | NOTCH3 | RNASE1 | GNLY | ELF3 | C7 | MTRNR2L8 | FGF7 | SOD2 |
| 16 | IGFBP6 | KIF5C | GBP1 | COL10A1 | HLA-DRA | ADM | PLAT | TPX2 | CRIP1 | ITGA6 | LAPTM5 | CLDN4 | CHI3L1 | IFI27 | MT3 | HP |
| 17 | FBLN2 | STAR | BST2 | COL5A2 | LXN | FTH1 | CCDC80 | HMGB2 | ITGA1 | SRGN | CD14 | MAL2 | ADH1B | CTSK | OST4 | CCDC71L |
| 18 | PLAC9 | TRABD2A | RNF213 | PALLD | EFEMP1 | SLC2A1 | SAA1 | CDKN3 | LHFPL6 | CDH5 | HLA-DQB1 | MUC1 | MTRNR2L12 | CHN1 | GAPDH | ALDH1A3 |
| 19 | FBLN5 | SYNPO2 | DDX58 | SFRP2 | CENPW | HSP90B1 | SLC39A8 | HMGB1 | C11orf96 | FLT1 | HLA-DQA1 | TACSTD2 | AC092069.1 | TMEM176A | LDHA | CFB |
| 20 | FBN1 | DKK3 | LAP3 | FN1 | ATF3 | NUPR1 | AQP1 | CKS2 | ANGPT2 | EMCN | AIF1 | SMIM22 | CYR61 | TMEM176B | PLIN2 | SLC39A8 |
| 21 | ABCA8 | CHL1 | IFI27 | MAFB | IER3 | LDHA | TIMP1 | SMC4 | MYH11 | MMRN1 | NKG7 | FOLR1 | LRRC75A | PVALB | TGFBI | MT1X |
| 22 | PDK4 | TCF21 | IFIH1 | INHBA | HLA-DRB1 | PLOD2 | CXCL6 | UBE2C | EPS8 | TM4SF18 | CCL4L2 | CP | POSTN | PRRX1 | TPI1 | CXCL8 |
| 23 | MGST1 | PRELP | MMP13 | COL3A1 | CYR61 | CA9 | S100A10 | TUBB | UACA | PTPRB | FCGR3A | S100A1 | ACTA2 | ISG20 | TMEM258 | PRG4 |
| 24 | FMO2 | GATM | XAF1 | HTRA1 | TIMP1 | IER3 | HLA-DRB1 | UBE2S | SPARCL1 | RAMP3 | CD52 | CST6 | CXCL12 | NTM | CTSC | ABI3BP |
| 25 | ZFP36 | SCN7A | EPSTI1 | SEPT11 | PIEZO2 | CA12 | SOD2 | NUCKS1 | PDGFA | SOX18 | CD2 | MSLN | GPX3 | SERPINF1 | TMA7 | CA12 |
| 26 | MT1A | ALKAL2 | OASL | PLAU | PTGIS | ENO1 | WFDC2 | DLGAP5 | NR2F2 | CALCRL | LTB | MAL | CFD | AEBP1 | UQCRQ | PLA2G2A |
| 27 | DCN | KLHDC8A | SAMD9 | TCF4 | ADAMTS1 | DDIT4 | PTGIS | TK1 | GJC1 | BTNL9 | ITGB2 | TCIM | WISP2 | PSME1 | EIF4EBP1 | SBSN |
| 28 | GPC3 | DIRAS3 | IFI35 | ANTXR1 | SAT1 | FAM162A | SAT1 | KPNA2 | ADGRF5 | ESAM | CORO1A | BCAM | TAGLN | MME | ATP5ME | CLDN1 |
| 29 | MFAP4 | DHRS2 | CCL11 | PRRX1 | SOD2 | G0S2 | RARRES1 | TUBB4B | PLAC9 | LMO2 | HCST | CLDN7 | SERPINF1 | TCF4 | PPP1R14B | DES |
| 30 | ABCA6 | FOXL2 | HERC5 | LUM | DES | AL133453.1 | SGK1 | CENPE | CARMN | GIMAP7 | GZMA | SCGB2A1 | APOD | CD63 | SERPINE1 | PTX3 |
| 31 | ABCA9 | NRXN2 | GBP4 | COLEC12 | WNT4 | TGFBI | MT1G | TUBA1C | PDGFRB | NOTCH4 | FYB1 | ERP27 | MALAT1 | LUM | CADM3 | CADM3 |
| 32 | DEPP1 | PALMD | OAS3 | PMEPA1 | EGFL6 | EGLN3 | RARRES3 | JPT1 | TINAGL1 | SLCO2A1 | CD69 | SFTA2 | NEAT1 | NCAM2 | ACTB | PDLIM1 |
| 33 | SCARA5 | LAMA2 | EIF2AK2 | SULF1 | NFKBIZ | UPP1 | CXCL1 | KIF20B | COX4I2 | MMRN2 | ARHGDIB | MUC16 | BGN | TMSB4X | C4orf48 | PDZK1IP1 |
| 34 | CXCL12 | PLCXD3 | MMP11 | DIO2 | RARRES3 | PGK1 | SELENOP | CKAP2 | HES4 | APLNR | SAMSN1 | HMGA1 | VMP1 | MMP2 | UBL5 | CHI3L2 |
| 35 | LHFPL6 | WIPF3 | LY6E | ITGB5 | JUN | PTGS2 | HLA-DRA | HMMR | TBX2 | ICAM2 | CD3D | S100A9 | MMP2 | ANTXR1 | S100A11 | CALB2 |
| 36 | SVEP1 | TMEM233 | PLSCR1 | FAP | METTL7A | ERRFI1 | CEMIP | SMC2 | CAV1 | ECSCR | LCP1 | TPI1 | MBNL1 | LINC01705 | HSD11B1 | PAPPA |
| 37 | PLPP3 | SIGLEC11 | CMPK2 | CDH11 | OSR1 | CLEC2B | SERPINB2 | NASP | STEAP4 | ROBO4 | MS4A6A | AGR2 | POLR2J3-1 | SOST | ENO1 | VTN |
| 38 | PRELP | RASL11B | OAS2 | MARCKS | C2 | ID2 | CFB | PCNA | MYL9 | CXorf36 | ALOX5AP | UCA1 | ITPRIPL2 | HTRA1 | SERF2 | OSR1 |
| 39 | TXNIP | PRKAR2B | SAMD9L | MMP2 | RHOU | HERPUD1 | IL6ST | HELLS | GUCY1A2 | NOSTRIN | CD53 | UPK1B | EIF3L | HLA-C | TMSB10 | EPGN |
| 40 | ITM2A | BMPR1B | TYMP | COL5A1 | IL6ST | WSB1 | DAB2 | PBK | SEPT4 | CYYR1 | SLA | UCP2 | SARS | TXN | COX7C | GRIA2 |
| 41 | CD34 | GREB1 | ISG20 | SPARC | SOD3 | CXCL8 | KRT7 | RRM2 | TAGLN | FAM167B | GZMB | SPINK1 | ZCCHC24 | THY1 | SSR4 | PI3 |
| 42 | RASD1 | PCSK6 | IFI44 | SDC1 | TXNIP | SLC16A3 | KLK11 | MAD2L1 | FAM162B | PCAT19 | CD37 | S100A14 | LARP1 | CTHRC1 | ATP5F1E | CXADR |
| 43 | EFEMP1 | ARX | TNFSF13B | TMEM158 | PLS3 | STC1 | MAF | ANLN | TFPI | TIE1 | CYTIP | CRIP1 | SPPL2A | SELENOM | TREM1 | PICSAR |
| 44 | FSTL1 | P3H2 | CTSK | NTM | CHI3L1 | SOD2 | CYB5A | CCNB2 | JAG1 | GIMAP4 | CCL3L1 | SCNN1A | COL6A3 | HLA-A | PTPRN | LINC01133 |
| 45 | COL14A1 | CDHR1 | PSMB9 | LRRC17 | NFKBIA | P4HB | SAA2 | H2AFV | S100A4 | MYCT1 | CD163 | TMEM238 | EIF3A | WNT10A | CXCL5 | UPK3B |
| 46 | TSHZ2 | ECEL1 | IL32 | THY1 | ZFP36 | C15orf48 | DES | CKS1B | PTP4A3 | ESM1 | MS4A7 | VAMP8 | ARID1B | LGALS1 | IL33 | FRAS1 |
| 47 | SOCS3 | HS3ST1 | IDO1 | MDK | FGF7 | BNIP3L | CD74 | DEK | PGF | RAPGEF5 | CD48 | ITGB8 | PCSK7 | HLA-B | SPOCD1 | MARCH3 |
| 48 | FBLN1 | ARHGEF28 | WARS | COL6A2 | CD74 | SERPINE2 | EFEMP1 | CDK1 | ENPEP | CCL14 | GPR183 | FXYD3 | PLTP | COL6A3 | P2RY6 | KLK11 |
| 49 | LAMA2 | ARHGAP25 | SP100 | GJA1 | IL1R1 | MANF | CLU | RAN | CD36 | MCTP1 | GMFG | SLC34A2 | NFYB | PTMA | C5orf46 | AADAC |
| 50 | CHRDL1 | SERPINA5 | TRIM22 | EPYC | TNFAIP3 | DDIT3 | IGFBP6 | DTYMK | AVPR1A | PREX2 | GIMAP4 | GSTP1 | LUM | B2M | PTGS1 | SERPINB3 |
enframe(sort(table(seu_obj$cluster_label))) %>%
mutate(name = ordered(name, levels = rev(name))) %>%
ggplot() +
geom_bar(aes(name, value), stat = "identity") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
labs(y = c("#cells"), x = "cluster")
alpha_lvl <- ifelse(nrow(plot_data) < 20000, 0.2, 0.1)
pt_size <- ifelse(nrow(plot_data) < 20000, 0.2, 0.05)
common_layers_disc <- list(
geom_point(size = pt_size, alpha = alpha_lvl),
NoAxes(),
guides(color = guide_legend(override.aes = list(size = 2, alpha = 1))),
labs(color = "")
)
common_layers_cont <- list(
geom_point(size = pt_size, alpha = alpha_lvl),
NoAxes(),
scale_color_gradientn(colors = viridis(9)),
guides(color = guide_colorbar())
)
ggplot(plot_data, aes(umapharmony_1, umapharmony_2, color = cluster_label)) +
common_layers_disc +
#facet_wrap(~therapy) +
ggtitle("Sub cluster")
my_subtypes <- names(clrs$cluster_label[[coi]])
my_subtypes <- c(my_subtypes, unlist(lapply(paste0("_", 1:3), function(x) paste0(my_subtypes, x)))) %>% .[!str_detect(., "doublet|dissociated|Mito.high|OV\\.[0-9]")]
my_subtypes <- my_subtypes[my_subtypes %in% unique(seu_obj$cluster_label)]
my_subtypes <- my_subtypes[my_subtypes %in% names(clrs$cluster_label[[coi]])]
cells_to_keep <- colnames(seu_obj)[seu_obj$cluster_label %in% my_subtypes]
seu_obj_sub <- subset(seu_obj, cells = cells_to_keep)
seu_obj_sub <- RunUMAP(seu_obj_sub, dims = 1:50, reduction = "harmony", reduction.name = "umapharmony", reduction.key = "umapharmony_")
seu_obj_sub$cluster_label <- seu_obj$cluster_label[colnames(seu_obj) %in% colnames(seu_obj_sub)]
write_rds(seu_obj_sub, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_processed_filtered.rds"))
seu_obj_sub <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_processed_filtered.rds"))
plot_data_sub <- as_tibble(FetchData(seu_obj_sub, c(myfeatures, "cluster_label"))) %>%
left_join(meta_tbl, by = "sample") %>%
mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-"),
tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite))) %>%
mutate(cell_id = colnames(seu_obj_sub),
cluster_label = ordered(cluster_label, levels = my_subtypes),
)
if (cell_sort == "CD45+") {
plot_data_sub <- filter(plot_data_sub, sort_parameters != "singlet, live, CD45-", !is.na(tumor_supersite))
}
if (cell_sort == "CD45-") {
plot_data_sub <- filter(plot_data_sub, sort_parameters != "singlet, live, CD45+", !is.na(tumor_supersite))
}
ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = cluster_label)) +
common_layers_disc +
ggtitle("Sub cluster") +
#facet_wrap(~cluster_label) +
scale_color_manual(values = clrs$cluster_label[[coi]])
ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = patient_id_short)) +
common_layers_disc +
ggtitle("Patient") +
# facet_wrap(~therapy) +
scale_color_manual(values = clrs$patient_id_short)
ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = tumor_supersite)) +
# geom_point(aes(umapharmony_1, umapharmony_2),
# color = "grey90", size = 0.01,
# data = select(plot_data_sub, -tumor_supersite)) +
common_layers_disc +
ggtitle("Site") +
# facet_wrap(~therapy) +
scale_color_manual(values = clrs$tumor_supersite)
write_tsv(select(plot_data_sub, cell_id, everything(), -umapharmony_1, -umapharmony_2, -contains("RNA_")), paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_embedding.tsv"))
signature_modules <- read_excel("_data/small/signatures/SPECTRUM v5 sub cluster markers.xlsx", sheet = 2, skip = 1, range = "M2:P100") %>%
gather(module, gene) %>%
na.omit() %>%
group_by(module) %>%
do(gene = c(.$gene)) %>%
{setNames(.$gene, .$module)}
signature_modules$ISG.module <- c("CCL5", "CXCL10", "IFNA1", "IFNB1", "ISG15", "IFI27L2", "SAMD9L")
## compute expression module scores
for (i in 1:length(signature_modules)) {
seu_obj_sub <- AddModuleScore(seu_obj_sub, features = signature_modules[i], name = names(signature_modules)[i])
seu_obj_sub[[names(signature_modules)[i]]] <- seu_obj_sub[[paste0(names(signature_modules)[i], "1")]]
seu_obj_sub[[paste0(names(signature_modules)[i], "1")]] <- NULL
print(paste(names(signature_modules)[i], "DONE"))
}
## [1] "CD8.Cytotoxic DONE"
## [1] "CD8.Dysfunctional DONE"
## [1] "CD8.Naive DONE"
## [1] "CD8.Predysfunctional DONE"
## [1] "ISG.module DONE"
## compute progeny scores
progeny_list <- seu_obj_sub@assays$RNA@data[VariableFeatures(seu_obj_sub),] %>%
as.matrix %>%
progeny %>%
as.data.frame %>%
as.list
names(progeny_list) <- make.names(paste0(names(progeny_list), ".pathway"))
for (i in 1:length(progeny_list)) {
seu_obj_sub <- AddMetaData(seu_obj_sub, metadata = progeny_list[[i]],
col.name = names(progeny_list)[i])
}
write_rds(seu_obj_sub, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_processed_filtered_annotated.rds"))
seu_obj_sub <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_processed_filtered_annotated.rds"))
marker_top_tbl <- marker_sheet[,-1] %>%
slice(1:10) %>%
as.list %>%
.[!str_detect(names(.), "doublet|dissociated|Mito.high|OV\\.[0-9]")] %>%
enframe("cluster_label_x", "gene") %>%
unnest(gene)
plot_data_markers <- as_tibble(FetchData(seu_obj_sub, c("cluster_label", myfeatures, unique(marker_top_tbl$gene)))) %>%
gather(gene, value, -c(1:(length(myfeatures)+1))) %>%
left_join(meta_tbl, by = "sample") %>%
mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-"),
tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite))) %>%
mutate(cluster_label = ordered(cluster_label, levels = my_subtypes)) %>%
group_by(cluster_label, gene) %>%
summarise(value = mean(value, na.rm = T)) %>%
group_by(gene) %>%
mutate(value = scales::rescale(value)) %>%
left_join(marker_top_tbl, by = "gene") %>%
mutate(cluster_label_x = ordered(cluster_label_x, levels = rev(names(clrs$cluster_label[[coi]]))))
ggplot(plot_data_markers) +
geom_tile(aes(gene, cluster_label, fill = value)) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
facet_grid(~cluster_label_x, scales = "free", space = "free") +
scale_fill_gradientn(colors = viridis(9)) +
labs(fill = "Scaled\nexpression") +
theme(aspect.ratio = 1,
axis.line = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank())
# ggsave(paste0("_fig/002_marker_heatmap_", coi, ".pdf"), width = nrow(marker_top_tbl)/6, height = 5)
comp_site_tbl <- plot_data_sub %>%
filter(!is.na(tumor_supersite)) %>%
group_by(cluster_label, tumor_supersite) %>%
tally %>%
group_by(tumor_supersite) %>%
mutate(nrel = n/sum(n)*100) %>%
ungroup
pnrel_site <- ggplot(comp_site_tbl) +
geom_bar(aes(tumor_supersite, nrel, fill = cluster_label),
stat = "identity", position = position_stack()) +
scale_y_continuous(expand = c(0, 0)) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
labs(fill = "Cluster", y = "Fraction [%]", x = "") +
scale_fill_manual(values = clrs$cluster_label[[coi]])
pnabs_site <- ggplot(comp_site_tbl) +
geom_bar(aes(tumor_supersite, n, fill = cluster_label),
stat = "identity", position = position_stack()) +
scale_y_continuous(expand = c(0, 0)) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
labs(fill = "Cluster", y = "# cells", x = "") +
scale_fill_manual(values = clrs$cluster_label[[coi]])
plot_grid(pnabs_site, pnrel_site, ncol = 2, align = "h")
# ggsave(paste0("_fig/02_deep_dive_", coi, "_comp_site.pdf"), width = 8, height = 4)
comp_tbl_sample_sort <- plot_data_sub %>%
group_by(tumor_subsite, tumor_supersite, patient_id, consensus_signature, therapy, cluster_label) %>%
tally %>%
group_by(tumor_subsite, tumor_supersite, patient_id, consensus_signature, therapy) %>%
mutate(nrel = n/sum(n)*100,
nsum = sum(n),
log10n = log10(n),
label_supersite = "Site",
label_mutsig = "Signature",
label_therapy = "Rx") %>%
ungroup %>%
arrange(desc(therapy), tumor_supersite) %>%
mutate(tumor_subsite_rx = paste0(tumor_subsite, "_", therapy)) %>%
mutate(tumor_subsite = ordered(tumor_subsite, levels = unique(tumor_subsite)),
tumor_subsite_rx = ordered(tumor_subsite_rx, levels = unique(tumor_subsite_rx))) %>%
arrange(patient_id) %>%
mutate(label_patient_id = ifelse(as.logical(as.numeric(fct_inorder(as.character(patient_id)))%%2), "Patient1", "Patient2"))
sample_id_x_tbl <- plot_data_sub %>%
mutate(sort_short_x = cell_sort) %>%
distinct(patient_id, sort_short_x, tumor_subsite, therapy, sample) %>%
unite(sample_id_x, patient_id, sort_short_x, tumor_subsite, therapy) %>%
arrange(sample_id_x)
comp_tbl_sample_sort %>%
mutate(sort_short_x = cell_sort) %>%
unite(sample_id_x, patient_id, sort_short_x, tumor_subsite_rx) %>%
select(sample_id_x, cluster_label, n, nrel, nsum) %>%
left_join(sample_id_x_tbl, by = "sample_id_x") %>%
write_tsv(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_subtype_compositions.tsv"))
ybreaks <- c(500, 1000, 2000, 4000, 6000, 8000, 10000, 15000, 20000)
max_cells_per_sample <- max(comp_tbl_sample_sort$nsum)
ymaxn <- ybreaks[max_cells_per_sample < ybreaks][1]
comp_plot_wrapper <- function(y = "nrel", switch = NULL) {
if (y == "nrel") ylab <- paste0("Fraction\nof cells [%]")
if (y == "n") ylab <- paste0("Number\nof cells")
p <- ggplot(comp_tbl_sample_sort,
aes_string("tumor_subsite_rx", y, fill = "cluster_label")) +
facet_grid(~patient_id, space = "free", scales = "free", switch = switch) +
coord_cartesian(clip = "off") +
scale_fill_manual(values = clrs$cluster_label[[coi]]) +
theme(axis.text.x = element_blank(),
axis.title.y = element_text(angle = 0, vjust = 0.5, hjust = 0.5,
margin = margin(0, -0.4, 0, 0, unit = "npc")),
axis.ticks.x = element_blank(),
axis.title.x = element_blank(),
axis.line.x = element_blank(),
strip.text.y = element_blank(),
strip.text.x = element_blank(),
strip.background.y = element_blank(),
strip.background.x = element_blank(),
plot.margin = margin(0, 0, 0, 0, "npc")) +
labs(x = "",
y = ylab) +
guides(fill = FALSE)
if (y == "nrel") p <- p +
geom_bar(stat = "identity") +
scale_y_continuous(expand = c(0, 0),
breaks = c(0, 50, 100),
labels = c("0", "50", "100"))
if (y == "n") p <- p +
geom_bar(stat = "identity", position = position_stack()) +
scale_y_continuous(expand = c(0, 0),
breaks = c(0, ymaxn/2, ymaxn),
limits = c(0, ymaxn),
labels = c("", ymaxn/2, ymaxn)) +
expand_limits(y = c(0, ymaxn)) +
theme(panel.grid.major.y = element_line(linetype = 1, color = "grey90", size = 0.5))
return(p)
}
common_label_layers <- list(
geom_tile(color = "white", size = 0.15),
theme(axis.text.x = element_blank(),
axis.ticks = element_blank(),
axis.title.x = element_blank(),
axis.line.x = element_blank(),
strip.text = element_blank(),
strip.background = element_blank(),
plot.margin = margin(0, 0, 0, 0, "npc")),
scale_y_discrete(expand = c(0, 0)),
labs(y = ""),
guides(fill = FALSE),
facet_grid(~patient_id,
space = "free", scales = "free")
)
comp_label_site <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_supersite, patient_id),
aes(tumor_subsite_rx, label_supersite,
fill = tumor_supersite)) +
scale_fill_manual(values = clrs$tumor_supersite) +
common_label_layers
comp_label_rx <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_therapy, consensus_signature, patient_id),
aes(tumor_subsite_rx, label_therapy,
fill = therapy)) +
scale_fill_manual(values = c(`post-Rx` = "gold3", `pre-Rx` = "steelblue")) +
common_label_layers
comp_label_mutsig <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_mutsig, consensus_signature, patient_id),
aes(tumor_subsite_rx, label_mutsig,
fill = consensus_signature)) +
scale_fill_manual(values = clrs$consensus_signature) +
common_label_layers
patient_label_tbl <- distinct(comp_tbl_sample_sort, patient_id, .keep_all = T)
comp_label_patient_id <- ggplot(patient_label_tbl, aes(tumor_subsite_rx, label_patient_id)) +
scale_fill_manual(values = clrs$consensus_signature) +
geom_text(aes(tumor_subsite_rx, label_patient_id, label = patient_id)) +
facet_grid(~patient_id,
space = "free", scales = "free") +
coord_cartesian(clip = "off") +
theme_void() +
theme(strip.text = element_blank(),
strip.background = element_blank(),
plot.margin = margin(0, 0, 0, 0, "npc"))
hist_plot_wrapper <- function(x = "nrel") {
if (x == "nrel") {
xlab <- paste0("Fraction of cells [%]")
bw <- 5
}
if (x == "log10n") {
xlab <- paste0("Number of cells")
bw <- 0.2
}
p <- ggplot(comp_tbl_sample_sort) +
ggridges::geom_density_ridges(
aes_string(x, "cluster_label", fill = "cluster_label"), color = "black",
stat = "binline", binwidth = bw, scale = 3) +
facet_grid(label_supersite~.,
space = "free", scales = "free") +
scale_fill_manual(values = clrs$cluster_label[[coi]]) +
theme(axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.title.y = element_blank(),
axis.line.y = element_blank(),
strip.text = element_blank(),
strip.background = element_blank(),
plot.margin = margin(0, 0, 0, 0, "npc")) +
scale_x_continuous(expand = c(0, 0)) +
scale_y_discrete(expand = c(0, 0)) +
guides(fill = FALSE) +
labs(x = xlab)
if (x == "log10n") p <- p + expand_limits(x = c(0, 3)) +
scale_x_continuous(expand = c(0, 0),
labels = function(x) parse(text = paste("10^", x)))
return(p)
}
pcomp1 <- comp_plot_wrapper("n")
pcomp2 <- comp_plot_wrapper("nrel")
pcomp_grid <- plot_grid(comp_label_patient_id,
pcomp1, pcomp2,
comp_label_site, comp_label_rx, comp_label_mutsig,
ncol = 1, align = "v",
rel_heights = c(0.15, 0.33, 0.33, 0.06, 0.06, 0.06))
phist1 <- hist_plot_wrapper("log10n")
pcomp_hist_grid <- ggdraw() +
draw_plot(pcomp_grid, x = 0.01, y = 0, width = 0.85, height = 1) +
draw_plot(phist1, x = 0.87, y = 0.05, width = 0.12, height = 0.8)
pcomp_hist_grid
# ggsave(paste0("_fig/02_composition_v7_",coi,".pdf"), pcomp_hist_grid, width = 10, height = 2)
comp_tbl_z <- comp_tbl_sample_sort %>%
filter(therapy == "pre-Rx",
!(tumor_supersite %in% c("Ascites", "Other"))) %>%
group_by(patient_id, cluster_label) %>%
arrange(patient_id, cluster_label, nrel) %>%
mutate(rank = row_number(nrel),
z_rank = scales::rescale(rank)) %>%
mutate(mean_nrel = mean(nrel, na.rm = T),
sd_nrel = sd(nrel, na.rm = T),
z_nrel = (nrel - mean_nrel) / sd_nrel) %>%
ungroup()
ggplot(comp_tbl_z) +
geom_boxplot(aes(tumor_supersite, z_nrel, color = tumor_supersite),
outlier.shape = NA) +
geom_point(aes(tumor_supersite, z_nrel, color = tumor_supersite), position = position_jitter(), size = 0.1) +
facet_grid(~cluster_label, scales = "free_x", space = "free_x") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
aspect.ratio = 5) +
scale_color_manual(values = clrs$tumor_supersite)
ggplot(comp_tbl_z) +
geom_boxplot(aes(tumor_supersite, z_rank, color = tumor_supersite),
outlier.shape = NA) +
geom_point(aes(tumor_supersite, z_rank, color = tumor_supersite), position = position_jitter(), size = 0.1) +
facet_grid(~cluster_label, scales = "free_x", space = "free_x") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
aspect.ratio = 5) +
scale_color_manual(values = clrs$tumor_supersite)
devtools::session_info()
## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 3.6.2 (2019-12-12)
## os Debian GNU/Linux 10 (buster)
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz Etc/UTC
## date 2021-03-09
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date lib
## abind 1.4-5 2016-07-21 [2]
## ape 5.3 2019-03-17 [2]
## assertthat 0.2.1 2019-03-21 [2]
## backports 1.1.10 2020-09-15 [1]
## bibtex 0.4.2.2 2020-01-02 [2]
## Biobase 2.46.0 2019-10-29 [2]
## BiocGenerics 0.32.0 2019-10-29 [2]
## BiocParallel 1.20.1 2019-12-21 [2]
## bitops 1.0-6 2013-08-17 [2]
## boot 1.3-24 2019-12-20 [3]
## broom 0.7.2 2020-10-20 [1]
## callr 3.4.2 2020-02-12 [1]
## car 3.0-8 2020-05-21 [1]
## carData 3.0-4 2020-05-22 [1]
## caTools 1.17.1.4 2020-01-13 [2]
## cellranger 1.1.0 2016-07-27 [2]
## class 7.3-15 2019-01-01 [3]
## cli 2.0.2 2020-02-28 [1]
## cluster 2.1.0 2019-06-19 [3]
## codetools 0.2-16 2018-12-24 [3]
## colorblindr * 0.1.0 2020-01-13 [2]
## colorspace * 1.4-2 2019-12-29 [2]
## cowplot * 1.0.0 2019-07-11 [2]
## crayon 1.3.4 2017-09-16 [1]
## curl 4.3 2019-12-02 [2]
## data.table 1.12.8 2019-12-09 [2]
## DBI 1.1.0 2019-12-15 [2]
## dbplyr 2.0.0 2020-11-03 [1]
## DelayedArray 0.12.2 2020-01-06 [2]
## DEoptimR 1.0-8 2016-11-19 [1]
## desc 1.2.0 2018-05-01 [2]
## destiny * 3.0.1 2020-01-16 [1]
## devtools 2.2.1 2019-09-24 [2]
## digest 0.6.25 2020-02-23 [1]
## dplyr * 1.0.2 2020-08-18 [1]
## e1071 1.7-3 2019-11-26 [1]
## ellipsis 0.3.1 2020-05-15 [1]
## evaluate 0.14 2019-05-28 [2]
## fansi 0.4.1 2020-01-08 [2]
## farver 2.0.3 2020-01-16 [1]
## fitdistrplus 1.0-14 2019-01-23 [2]
## forcats * 0.5.0 2020-03-01 [1]
## foreign 0.8-74 2019-12-26 [3]
## formattable 0.2.0.1 2016-08-05 [1]
## fs 1.5.0 2020-07-31 [1]
## future 1.15.1 2019-11-25 [2]
## future.apply 1.4.0 2020-01-07 [2]
## gbRd 0.4-11 2012-10-01 [2]
## gdata 2.18.0 2017-06-06 [2]
## generics 0.0.2 2018-11-29 [2]
## GenomeInfoDb 1.22.0 2019-10-29 [2]
## GenomeInfoDbData 1.2.2 2020-01-14 [2]
## GenomicRanges 1.38.0 2019-10-29 [2]
## ggplot.multistats 1.0.0 2019-10-28 [1]
## ggplot2 * 3.3.2 2020-06-19 [1]
## ggrepel 0.8.1 2019-05-07 [2]
## ggridges 0.5.2 2020-01-12 [2]
## ggthemes 4.2.0 2019-05-13 [1]
## globals 0.12.5 2019-12-07 [2]
## glue 1.3.2 2020-03-12 [1]
## gplots 3.0.1.2 2020-01-11 [2]
## gridExtra 2.3 2017-09-09 [2]
## gtable 0.3.0 2019-03-25 [2]
## gtools 3.8.1 2018-06-26 [2]
## haven 2.3.1 2020-06-01 [1]
## hexbin 1.28.0 2019-11-11 [2]
## hms 0.5.3 2020-01-08 [1]
## htmltools 0.5.1.1 2021-01-22 [1]
## htmlwidgets 1.5.1 2019-10-08 [2]
## httr 1.4.2 2020-07-20 [1]
## ica 1.0-2 2018-05-24 [2]
## igraph 1.2.5 2020-03-19 [1]
## IRanges 2.20.2 2020-01-13 [2]
## irlba 2.3.3 2019-02-05 [2]
## jsonlite 1.7.1 2020-09-07 [1]
## KernSmooth 2.23-16 2019-10-15 [3]
## knitr 1.26 2019-11-12 [2]
## labeling 0.3 2014-08-23 [2]
## laeken 0.5.1 2020-02-05 [1]
## lattice 0.20-38 2018-11-04 [3]
## lazyeval 0.2.2 2019-03-15 [2]
## leiden 0.3.1 2019-07-23 [2]
## lifecycle 0.2.0 2020-03-06 [1]
## listenv 0.8.0 2019-12-05 [2]
## lmtest 0.9-37 2019-04-30 [2]
## lsei 1.2-0 2017-10-23 [2]
## lubridate 1.7.9.2 2020-11-13 [1]
## magrittr * 2.0.1 2020-11-17 [1]
## MASS 7.3-51.5 2019-12-20 [3]
## Matrix 1.2-18 2019-11-27 [3]
## matrixStats 0.56.0 2020-03-13 [1]
## memoise 1.1.0 2017-04-21 [2]
## metap 1.2 2019-12-08 [2]
## mnormt 1.5-5 2016-10-15 [2]
## modelr 0.1.8 2020-05-19 [1]
## multcomp 1.4-12 2020-01-10 [2]
## multtest 2.42.0 2019-10-29 [2]
## munsell 0.5.0 2018-06-12 [2]
## mutoss 0.1-12 2017-12-04 [2]
## mvtnorm 1.0-12 2020-01-09 [2]
## nlme 3.1-143 2019-12-10 [3]
## nnet 7.3-12 2016-02-02 [3]
## npsurv 0.4-0 2017-10-14 [2]
## numDeriv 2016.8-1.1 2019-06-06 [2]
## openxlsx 4.1.5 2020-05-06 [1]
## pbapply 1.4-2 2019-08-31 [2]
## pcaMethods 1.78.0 2019-10-29 [2]
## pillar 1.4.6 2020-07-10 [1]
## pkgbuild 1.0.6 2019-10-09 [2]
## pkgconfig 2.0.3 2019-09-22 [1]
## pkgload 1.0.2 2018-10-29 [2]
## plotly 4.9.1 2019-11-07 [2]
## plotrix 3.7-7 2019-12-05 [2]
## plyr 1.8.5 2019-12-10 [2]
## png 0.1-7 2013-12-03 [2]
## prettyunits 1.1.1 2020-01-24 [1]
## processx 3.4.2 2020-02-09 [1]
## progeny * 1.11.3 2020-10-22 [1]
## proxy 0.4-24 2020-04-25 [1]
## ps 1.3.2 2020-02-13 [1]
## purrr * 0.3.4 2020-04-17 [1]
## R.methodsS3 1.7.1 2016-02-16 [2]
## R.oo 1.23.0 2019-11-03 [2]
## R.utils 2.9.2 2019-12-08 [2]
## R6 2.4.1 2019-11-12 [1]
## ranger 0.12.1 2020-01-10 [1]
## RANN 2.6.1 2019-01-08 [2]
## rappdirs 0.3.1 2016-03-28 [2]
## RColorBrewer 1.1-2 2014-12-07 [2]
## Rcpp 1.0.4 2020-03-17 [1]
## RcppAnnoy 0.0.16 2020-03-08 [1]
## RcppEigen 0.3.3.7.0 2019-11-16 [2]
## RcppHNSW 0.2.0 2019-09-20 [2]
## RcppParallel 4.4.4 2019-09-27 [2]
## RCurl 1.98-1.1 2020-01-19 [1]
## Rdpack 0.11-1 2019-12-14 [2]
## readr * 1.4.0 2020-10-05 [1]
## readxl * 1.3.1 2019-03-13 [2]
## rematch 1.0.1 2016-04-21 [2]
## remotes 2.1.0 2019-06-24 [2]
## reprex 0.3.0 2019-05-16 [2]
## reshape2 1.4.3 2017-12-11 [2]
## reticulate 1.14 2019-12-17 [2]
## rio 0.5.16 2018-11-26 [1]
## rlang 0.4.8 2020-10-08 [1]
## rmarkdown 2.0 2019-12-12 [2]
## robustbase 0.93-6 2020-03-23 [1]
## ROCR 1.0-7 2015-03-26 [2]
## rprojroot 1.3-2 2018-01-03 [2]
## RSpectra 0.16-0 2019-12-01 [2]
## rstudioapi 0.11 2020-02-07 [1]
## rsvd 1.0.3 2020-02-17 [1]
## Rtsne 0.15 2018-11-10 [2]
## rvest 0.3.6 2020-07-25 [1]
## S4Vectors 0.24.2 2020-01-13 [2]
## sandwich 2.5-1 2019-04-06 [2]
## scales 1.1.0 2019-11-18 [2]
## scatterplot3d 0.3-41 2018-03-14 [1]
## sctransform 0.2.1 2019-12-17 [2]
## SDMTools 1.1-221.2 2019-11-30 [2]
## sessioninfo 1.1.1 2018-11-05 [2]
## Seurat * 3.1.2 2019-12-12 [2]
## SingleCellExperiment 1.8.0 2019-10-29 [2]
## smoother 1.1 2015-04-16 [1]
## sn 1.5-4 2019-05-14 [2]
## sp 1.4-2 2020-05-20 [1]
## stringi 1.5.3 2020-09-09 [1]
## stringr * 1.4.0 2019-02-10 [1]
## SummarizedExperiment 1.16.1 2019-12-19 [2]
## survival 3.1-8 2019-12-03 [3]
## testthat 2.3.2 2020-03-02 [1]
## TFisher 0.2.0 2018-03-21 [2]
## TH.data 1.0-10 2019-01-21 [2]
## tibble * 3.0.4 2020-10-12 [1]
## tidyr * 1.1.2 2020-08-27 [1]
## tidyselect 1.1.0 2020-05-11 [1]
## tidyverse * 1.3.0 2019-11-21 [2]
## tsne 0.1-3 2016-07-15 [2]
## TTR 0.23-6 2019-12-15 [1]
## usethis 1.5.1 2019-07-04 [2]
## uwot 0.1.5 2019-12-04 [2]
## vcd 1.4-7 2020-04-02 [1]
## vctrs 0.3.5 2020-11-17 [1]
## VIM 6.0.0 2020-05-08 [1]
## viridis * 0.5.1 2018-03-29 [2]
## viridisLite * 0.3.0 2018-02-01 [2]
## withr 2.3.0 2020-09-22 [1]
## xfun 0.12 2020-01-13 [2]
## xml2 1.3.2 2020-04-23 [1]
## xts 0.12-0 2020-01-19 [1]
## XVector 0.26.0 2019-10-29 [2]
## yaml 2.2.1 2020-02-01 [1]
## zip 2.0.4 2019-09-01 [1]
## zlibbioc 1.32.0 2019-10-29 [2]
## zoo 1.8-7 2020-01-10 [2]
## source
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## Bioconductor
## Bioconductor
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## Github (clauswilke/colorblindr@1ac3d4d)
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## Bioconductor
## Bioconductor
## Bioconductor
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##
## [1] /home/uhlitzf/R/lib
## [2] /usr/local/lib/R/site-library
## [3] /usr/local/lib/R/library